Courses

EE 1. The Science of Data, Signals, and Information.
9 units (3-0-6); third term.
Electrical Engineering has given rise to many key developments at the interface between the physical world and the information world. Fundamental ideas in data acquisition, sampling, signal representation, and quantification of information have their origin in electrical engineering.This course introduces these ideas and discusses signal representations, the interplay between time and frequency domains, difference equations and filtering, noise and denoising, data transmission over channels with limited capacity, signal quantization, feedback and neural networks, and how humans interpret data and information. Applications in various areas of science and engineering are covered. Satisfies the menu requirement of the Caltech core curriculum.
Instructor: Vaidyanathan.

EE 2. Electrical Engineering Entrepreneurial and Research Seminar.
1 unit; second term; Required for EE undergraduates.
Weekly seminar given by successful entrepreneurs and EE faculty, broadly describing their path to success and introducing different areas of research in electrical engineering: circuits and VLSI, communications, control, devices, images and vision, information theory, learning and pattern recognition, MEMS and micromachining, networks, electromagnetics and opto-electronics, RF and microwave circuits and antennas, robotics and signal processing, specifically, research going on at Caltech and in the industry.
Instructor: Emami.

EE/ME 7. Introduction to Mechatronics.
6 units (2-3-1); first term.
Mechatronics is the multi-disciplinary design of electro-mechanical systems. This course is intended to give the student a basic introduction to such systems. The course will focus on the implementations of sensor and actuator systems, the mechanical devices involved and the electrical circuits needed to interface with them. The class will consist of lectures and short labs where the student will be able to investigate the concepts discussed in lecture. Topics covered include motors, piezoelectric devices, light sensors, ultrasonic transducers, and navigational sensors such as accelerometers and gyroscopes. Graded pass/fail.
Instructor: George.

EE/CS 10 ab. Introduction to Digital Logic and Embedded Systems.
6 units (2-3-1); second, third terms.
This course is intended to give the student a basic understanding of the major hardware and software principles involved in the specification and design of embedded systems. The course will cover basic digital logic, programmable logic devices, CPU and embedded system architecture, and embedded systems programming principles (interfacing to hardware, events, user interfaces, and multi-tasking).
Instructor: George.

EE 13. Electronic System Prototyping.
3 units (0-3-0); first term.
This course is intended to introduce the student to the technologies and techniques used to fabricate electronic systems. The course will cover the skills needed to use standard CAD tools for circuit prototyping. This includes schematic capture and printed circuit board design. Additionally, soldering techniques will be covered for circuit fabrication as well as some basic debugging skills. Each student will construct a system from schematic to PCB to soldering the final prototype.
Instructor: George.

EE/CS 53. Microprocessor Project Laboratory.
12 units (0-12-0); first, second, third terms.
Prerequisites: EE/CS 10 ab or equivalent.
A project laboratory to permit the student to select, design, and build a microprocessor-based system. The student is expected to take a project from proposal through design and implementation (possibly including PCB fabrication) to final review and documentation. May be repeated for credit.
Instructor: George.

CS/EE/ME 75 abc. Multidisciplinary Systems Engineering.
3 units (2-0-1), 6 units (2-0-4), or 9 units (2-0-7) first term; 6 units (2-3-1), 9 units (2-6-1), or 12 units (2-9-1) second and third terms.
This course presents the fundamentals of modern multidisciplinary systems engineering in the context of a substantial design project. Students from a variety of disciplines will conceive, design, implement, and operate a system involving electrical, information, and mechanical engineering components. Specific tools will be provided for setting project goals and objectives, managing interfaces between component subsystems, working in design teams, and tracking progress against tasks. Students will be expected to apply knowledge from other courses at Caltech in designing and implementing specific subsystems. During the first two terms of the course, students will attend project meetings and learn some basic tools for project design, while taking courses in CS, EE, and ME that are related to the course project. During the third term, the entire team will build, document, and demonstrate the course design project, which will differ from year to year. Freshmen must receive permission from the lead instructor to enroll. Not offered 2018-19.

EE 80 abc. Senior Thesis.
9 units; first, second, third terms.
Prerequisites: instructor's permission, which should be obtained during the junior year to allow sufficient time for planning the research.
Individual research project, carried out under the supervision of a member of the electrical engineering or computer science faculty. Project must include significant design effort. Written report required. Open only to senior electrical engineering, computer science, or electrical and computer engineering majors. Not offered on a pass/fail basis.
Instructor: Staff.

EE 91 ab. Experimental Projects in Electronic Circuits.
Units by arrangement; first, second terms.
Prerequisites: EE 45. Recommended: EE/CS 10 ab, and EE/MedE 114 ab (may be taken concurrently).
Open to seniors; others only with instructor's permission. An opportunity to do advanced original projects in analog or digital electronics and electronic circuits. Selection of significant projects, the engineering approach, modern electronic techniques, demonstration and review of a finished product. DSP/microprocessor development support and analog/digital CAD facilities available. Text: literature references.
Instructor: Megdal.

EE 99. Advanced Work in Electrical Engineering.
Units to be arranged.
Special problems relating to electrical engineering will be arranged. For undergraduates; students should consult with their advisers. Graded pass/fail.

EE 105 abc. Electrical Engineering Seminar.
1 unit; first, second, third terms.
All candidates for the M.S. degree in electrical engineering are required to attend any graduate seminar in any division each week of each term. Graded pass/fail.
Instructor: Emami.

EE 110 abc. Embedded Systems Design Laboratory.
9 units (3-4-2); first, second, third terms.
The student will design, build, and program a specified microprocessor-based embedded system. This structured laboratory is organized to familiarize the student with large-scale digital and embedded system design, electronic circuit construction techniques, modern development facilities, and embedded systems programming. The lectures cover topics in embedded system design such as display technologies, interfacing to analog signals, communication protocols, PCB design, and programming in high-level and assembly languages. Given in alternate years; not offered 2018-19.
Instructor: George.

EE 111. Signal-Processing Systems and Transforms.
9 units (3-0-6); first term.
Prerequisites: Ma 1.
An introduction to continuous and discrete time signals and systems with emphasis on digital signal processing systems. Study of the Fourier transform, Fourier series, z-transforms, and the fast Fourier transform as applied in electrical engineering. Sampling theorems for continuous to discrete-time conversion. Difference equations for digital signal processing systems, digital system realizations with block diagrams, analysis of transient and steady state responses, and connections to other areas in science and engineering.
Instructor: Vaidyanathan.

EE 113. Feedback and Control Circuits.
9 units (3-3-3); third term.
Prerequisites: EE 45 or equivalent.
This class studies the design and implementation of feedback and control circuits. The course begins with an introduction to basic feedback circuits, using both op amps and transistors. These circuits are used to study feedback principles, including circuit topologies, stability, and compensation. Following this, basic control techniques and circuits are studied, including PID (Proportional-Integrated-Derivative) control, digital control, and fuzzy control. There is a significant laboratory component to this course, in which the student will be expected to design, build, analyze, test, and measure the circuits and systems discussed in the lectures.
Instructor: George.

ACM/EE/IDS 116. Introduction to Probability Models.
9 units (3-1-5); first term.
Prerequisites: Ma 2, Ma 3.
This course introduces students to the fundamental concepts, methods, and models of applied probability and stochastic processes. The course is application oriented and focuses on the development of probabilistic thinking and intuitive feel of the subject rather than on a more traditional formal approach based on measure theory. The main goal is to equip science and engineering students with necessary probabilistic tools they can use in future studies and research. Topics covered include sample spaces, events, probabilities of events, discrete and continuous random variables, expectation, variance, correlation, joint and marginal distributions, independence, moment generating functions, law of large numbers, central limit theorem, random vectors and matrices, random graphs, Gaussian vectors, branching, Poisson, and counting processes, general discrete- and continuous-timed processes, auto- and cross-correlation functions, stationary processes, power spectral densities.
Instructor: Zuev.

Ph/APh/EE/BE 118 abc. Physics of Measurement.
9 units (3-0-6); first, second terms.
Prerequisites: Ph 127, APh 105, or equivalent, or permission from instructor..
This course focuses on exploring the fundamental underpinnings of experimental measurements from the perspectives of responsivity, noise, backaction, and information. Its overarching goal is to enable students to critically evaluate real measurement systems, and to determine the ultimate fundamental and practical limits to information that can be extracted from them. Topics will include physical signal transduction and responsivity, fundamental noise processes, modulation, frequency conversion, synchronous detection, signal-sampling techniques, digitization, signal transforms, spectral analyses, and correlations. The first term will cover the essential fundamental underpinnings, while topics in second term will include examples from optical methods, high-frequency and fast temporal measurements, biological interfaces, signal transduction, biosensing, and measurements at the quantum limit. Part c not offered in 2018-19.
Instructor: Roukes.

EE/CS 119 abc. Advanced Digital Systems Design.
9 units (3-3-3); first, second term.
Prerequisites: EE/CS 10 a or CS 24.
Advanced digital design as it applies to the design of systems using PLDs and ASICs (in particular, gate arrays and standard cells). The course covers both design and implementation details of various systems and logic device technologies. The emphasis is on the practical aspects of ASIC design, such as timing, testing, and fault grading. Topics include synchronous design, state machine design, ALU and CPU design, application-specific parallel computer design, design for testability, PALs, FPGAs, VHDL, standard cells, timing analysis, fault vectors, and fault grading. Students are expected to design and implement both systems discussed in the class as well as self-proposed systems using a variety of technologies and tools.
Instructor: George.

EE 121. Computational Signal Processing..
(3-0-9); first.
Prerequisites: EE 111, ACM/EE/IDS 116, ACM/IDS 104.
The role of computation in the acquisition, representation, and processing of signals. The course develops methodology based on linear algebra and optimization, with an emphasis on the interplay between structure, algorithms, and accuracy in the design and analysis of the methods. Specific topics covered include deterministic and stochastic signal models, statistical signal processing, inverse problems, and regularization. Problems arising in contemporary applications in the sciences and engineering are discussed, although the focus is on the common abstractions and methodological frameworks that are employed in the solution of these problems.
Instructor: Chandrasekaran.

EE/MedE 124. Mixed-mode Integrated Circuits.
9 units (3-0-6); first term.
Prerequisites: EE 45 a or equivalent.
Introduction to selected topics in mixed-signal circuits and systems in highly scaled CMOS technologies. Design challenges and limitations in current and future technologies will be discussed through topics such as clocking (PLLs and DLLs), clock distribution networks, sampling circuits, high-speed transceivers, timing recovery techniques, equalization, monitor circuits, power delivery, and converters (A/D and D/A). A design project is an integral part of the course.
Instructor: Emami.

EE/Ma/CS/IDS 127. Error-Correcting Codes.
9 units (3-0-6); second term.
Prerequisites: Ma 2.
This course develops from first principles the theory and practical implementation of the most important techniques for combating errors in digital transmission or storage systems. Topics include algebraic block codes, e.g., Hamming, BCH, Reed-Solomon (including a self-contained introduction to the theory of finite fields); and the modern theory of sparse graph codes with iterative decoding, e.g. LDPC codes, turbo codes. The students will become acquainted with encoding and decoding algorithms, design principles and performance evaluation of codes.
Instructor: Kostina.

APh/EE 132. Special Topics in Photonics and Optoelectronics.
9 units (3-0-6); third term.
Interaction of light and matter, spontaneous and stimulated emission, laser rate equations, mode-locking, Q-switching, semiconductor lasers. Optical detectors and amplifiers; noise characterization of optoelectronic devices. Propagation of light in crystals, electro-optic effects and their use in modulation of light; introduction to nonlinear optics. Optical properties of nanostructures. Not offered 2018-2019.

CS/EE/ME 134. Autonomy.
9 units (3-0-6); third term.
This course covers the basics of autonomy at the intersection of computer vision, machine learning and robotics. It includes selected topics from each of these domains, and their integration points. The lectures will be accompanied by a project that will integrate these ideas on hardware and result in a final demonstration of the concepts studied in the course. Not offered 2018-19.

CS/EE/IDS 143. Communication Networks.
9 units (3-3-3); first term.
Prerequisites: Ma 2, Ma 3, CS 24 and CS 38, or instructor permission.
This course focuses on the link layer (two) through the transport layer (four) of Internet protocols. It has two distinct components, analytical and systems. In the analytical part, after a quick summary of basic mechanisms on the Internet, we will focus on congestion control and explain: (1) How to model congestion control algorithms? (2) Is the model well defined? (3) How to characterize the equilibrium points of the model? (4) How to prove the stability of the equilibrium points? We will study basic results in ordinary differential equations, convex optimization, Lyapunov stability theorems, passivity theorems, gradient descent, contraction mapping, and Nyquist stability theory. We will apply these results to prove equilibrium and stability properties of the congestion control models and explore their practical implications. In the systems part, the students will build a software simulator of Internet routing and congestion control algorithms. The goal is not only to expose students to basic analytical tools that are applicable beyond congestion control, but also to demonstrate in depth the entire process of understanding a physical system, building mathematical models of the system, analyzing the models, exploring the practical implications of the analysis, and using the insights to improve the design.
Instructors: Low, Murray, Ralph.

CMS/CS/EE/IDS 144. Networks: Structure & Economics.
12 units (3-3-6); second term.
Prerequisites: Ma 2, Ma 3, Ma/CS 6 a, and CS 38, or instructor permission.
Social networks, the web, and the internet are essential parts of our lives and we all depend on them every day, but do you really know what makes them work? This course studies the "big" ideas behind our networked lives. Things like, what do networks actually look like (and why do they all look the same)? How do search engines work? Why do memes spread the way they do? How does web advertising work? For all these questions and more, the course will provide a mixture of both mathematical analysis and hands-on labs. The course assumes students are comfortable with graph theory, probability, and basic programming.
Instructor: Wierman.

CS/EE 145. Projects in Networking.
9 units (0-0-9); third term.
Prerequisites: Either CMS/CS/EE/IDS 144 or CS/IDS 142 in the preceding term, or instructor permission.
Students are expected to execute a substantial project in networking, write up a report describing their work, and make a presentation.
Instructors: Ralph, Wierman.

CS/EE 146. Advanced Networking.
9 units (3-3-3); third term.
Prerequisites: CS/EE/IDS 143 or instructor's permission.
This is a research-oriented course meant for undergraduates and beginning graduate students who want to learn about current research topics in networks such as the Internet, power networks, social networks, etc. The topics covered in the course will vary, but will be pulled from current research topics in the design, analysis, control, and optimization of networks, protocols, and Internet applications. Usually offered in alternate years. Not offered 2018-19.

EE/CS 147. Digital Ventures Design.
9 units (3-3-3); first term.
Prerequisites: none.
This course aims to offer the scientific foundations of analysis, design, development, and launching of innovative digital products and study elements of their success and failure. The course provides students with an opportunity to experience combined team-based design, engineering, and entrepreneurship. The lectures present a disciplined step-by-step approach to develop new ventures based on technological innovation in this space, and with invited speakers, cover topics such as market analysis, user/product interaction and design, core competency and competitive position, customer acquisition, business model design, unit economics and viability, and product planning. Throughout the term students will work within an interdisciplinary team of their peers to conceive an innovative digital product concept and produce a business plan and a working prototype. The course project culminates in a public presentation and a final report. Every year the course and projects focus on a particular emerging technology theme. Not offered 2018-19.
Instructor: Staff.

EE/CNS/CS 148. Selected Topics in Computational Vision.
9 units (3-0-6); third term.
Prerequisites: undergraduate calculus, linear algebra, geometry, statistics, computer programming.
The class will focus on an advanced topic in computational vision: recognition, vision-based navigation, 3-D reconstruction. The class will include a tutorial introduction to the topic, an exploration of relevant recent literature, and a project involving the design, implementation, and testing of a vision system. Not offered 2018-19.
Instructor: Perona.

EE 150. Topics in Electrical Engineering.
Units to be arranged; terms to be arranged.
Content will vary from year to year, at a level suitable for advanced undergraduate or beginning graduate students. Topics will be chosen according to the interests of students and staff. Visiting faculty may present all or portions of this course from time to time.
Instructor: Staff.

EE 152. High Frequency Systems Laboratory.
12 units (2-3-7); first term.
Prerequisites: EE 45 or equivalent. EE 153 recommended.
The student will develop a strong, working knowledge of high-frequency systems covering RF and microwave frequencies. The essential building blocks of these systems will be studied along with the fundamental system concepts employed in their use. The first part of the course will focus on the design and measurement of core system building blocks; such as filters, amplifiers, mixers, and oscillators. Lectures will introduce key concepts followed by weekly laboratory sessions where the student will design and characterize these various system components. During the second part of the course, the student will develop their own high-frequency system, focused on a topic within remote sensing, communications, radar, or one within their own field of research. Not offered 2018-19.
Instructor: Russell.

EE 154 ab. Practical Electronics for Space Applications.
9 units (2-3-4); second and third terms.
Part a: Subsystem Design: Students will be exposed to design for subsystem electronics in the space environment, including an understanding of the space environment, common approaches for low cost spacecraft, atmospheric / analogue testing, and discussions of risk. Emphasis on a practical exposure to early subsystem design for a TRL 3-4 effort. Part b: Subsystems to System Interfacing: Builds upon the first term by extending subsystems to be compatible with "spacecraft", including a near-space "flight" of prototype subsystems on a high-altitude balloon flight. Focus on qualification for the flight environment appropriate to a TRL 4-5 effort.
Instructor: Klesh.

CMS/CS/CNS/EE/IDS 155. Machine Learning & Data Mining.
12 units (3-3-6); second term.
Prerequisites: CS/CNS/EE 156 a. Having a sufficient background in algorithms, linear algebra, calculus, probability, and statistics, is highly recommended.
This course will cover popular methods in machine learning and data mining, with an emphasis on developing a working understanding of how to apply these methods in practice. The course will focus on basic foundational concepts underpinning and motivating modern machine learning and data mining approaches. We will also discuss recent research developments
Instructor: Yue.

CS/CNS/EE 156 ab. Learning Systems.
9 units (3-0-6); first, third terms.
Prerequisites: Ma 2 and CS 2, or equivalent.
Introduction to the theory, algorithms, and applications of automated learning. How much information is needed to learn a task, how much computation is involved, and how it can be accomplished. Special emphasis will be given to unifying the different approaches to the subject coming from statistics, function approximation, optimization, pattern recognition, and neural networks.
Instructor: Abu-Mostafa.

EE/Ae 157 ab. Introduction to the Physics of Remote Sensing.
9 units (3-0-6); first, second terms.
Prerequisites: Ph 2 or equivalent.
An overview of the physics behind space remote sensing instruments. Topics include the interaction of electromagnetic waves with natural surfaces, including scattering of microwaves, microwave and thermal emission from atmospheres and surfaces, and spectral reflection from natural surfaces and atmospheres in the near-infrared and visible regions of the spectrum. The class also discusses the design of modern space sensors and associated technology, including sensor design, new observation techniques, ongoing developments, and data interpretation. Examples of applications and instrumentation in geology, planetology, oceanography, astronomy, and atmospheric research.
Instructor: van Zyl.

Ge/EE/ESE 157 c. Remote Sensing for Environmental and Geological Applications.
9 units (3-3-3); third term.
Analysis of electromagnetic radiation at visible, infrared, and radio wavelengths for interpretation of the physical and chemical characteristics of the surfaces of Earth and other planets. Topics: interaction of light with materials, spectroscopy of minerals and vegetation, atmospheric removal, image analysis, classification, and multi-temporal studies. This course does not require but is complementary to EE 157ab with emphasis on applications for geological and environmental problems, using data acquired from airborne and orbiting remote sensing platforms. Students will work with digital remote sensing datasets in the laboratory and there will be one field trip.
Instructor: Ehlmann.

CS/CNS/EE/IDS 159. Advanced Topics in Machine Learning.
9 units (3-0-6); third term.
Prerequisites: CS 155; strong background in statistics, probability theory, algorithms, and linear algebra; background in optimization is a plus as well.
This course focuses on current topics in machine learning research. This is a paper reading course, and students are expected to understand material directly from research articles. Students are also expected to present in class, and to do a final project.
Instructor: Yue.

EE/CS 161. Big Data Networks.
9 units (3-0-6); third term.
Prerequisites: Linear Algebra ACM/IDS 104 and Probability and Random Processes ACM/EE/IDS 116 or their equivalents.
Next generation networks will have tens of billions of nodes forming cyber-physical systems and the Internet of Things. A number of fundamental scientific and technological challenges must be overcome to deliver on this vision. This course will focus on (1) How to boost efficiency and reliability in large networks; the role of network coding, distributed storage, and distributed caching; (2) How to manage wireless access on a massive scale; modern random access and topology formation techniques; and (3) New vistas in big data networks, including distributed computing over networks and crowdsourcing. A selected subset of these problems, their mathematical underpinnings, state-of-the-art solutions, and challenges ahead will be covered. Given in alternate years. Not offered 2018-19.
Instructor: Hassibi.

EE/CS/IDS 167. Introduction to Data Compression and Storage.
9 units (3-0-6); third term.
Prerequisites: Ma 3 or ACM/EE/IDS 116.
The course will introduce the students to the basic principles and techniques of codes for data compression and storage. The students will master the basic algorithms used for lossless and lossy compression of digital and analog data and the major ideas behind coding for flash memories. Topics include the Huffman code, the arithmetic code, Lempel-Ziv dictionary techniques, scalar and vector quantizers, transform coding; codes for constrained storage systems. Given in alternate years; offered 2018-19.
Instructor: Kostina.

EE/CS/MedE 175. Digital Circuits Analysis and Design with Complete VHDL and RTL Approach.
9 units (3-6-0); third term.
Prerequisites: medium to advanced knowledge of digital electronics.
A careful balance between synthesis and analysis in the development of digital circuits plus a truly complete coverage of the VHDL language. The RTL (register transfer level) approach. Study of FPGA devices and comparison to ASIC alternatives. Tutorials of software and hardware tools employed in the course. VHDL infrastructure, including lexical elements, data types, operators, attributes, and complex data structures. Detailed review of combinational circuits followed by full VHDL coverage for combinational circuits plus recommended design practices. Detailed review of sequential circuits followed by full VHDL coverage for sequential circuits plus recommended design practices. Detailed review of state machines followed by full VHDL coverage and recommended design practices. Construction of VHDL libraries. Hierarchical design and practice on the hard task of project splitting. Automated simulation using VHDL testbenches. Designs are implemented in state-of-the-art FPGA boards. Not Offered 2018-19.
Instructor: Pedroni.

EE/APh 180. Nanotechnology.
6 units (3-0-3); first term.
This course will explore the techniques and applications of nanofabrication and miniaturization of devices to the smallest scale. It will be focused on the understanding of the technology of miniaturization, its history and present trends towards building devices and structures on the nanometer scale. Examples of applications of nanotechnology in the electronics, communications, data storage and sensing world will be described, and the underlying physics as well as limitations of the present technology will be discussed.
Instructor: Scherer.

CNS/Bi/EE/CS/NB 186. Vision: From Computational Theory to Neuronal Mechanisms.
12 units (4-4-4); second term.
Lecture, laboratory, and project course aimed at understanding visual information processing, in both machines and the mammalian visual system. The course will emphasize an interdisciplinary approach aimed at understanding vision at several levels: computational theory, algorithms, psychophysics, and hardware (i.e., neuroanatomy and neurophysiology of the mammalian visual system). The course will focus on early vision processes, in particular motion analysis, binocular stereo, brightness, color and texture analysis, visual attention and boundary detection. Students will be required to hand in approximately three homework assignments as well as complete one project integrating aspects of mathematical analysis, modeling, physiology, psychophysics, and engineering. Given in alternate years; Not Offered 2018-19.
Instructors: Meister, Perona, Shimojo.

EE/MedE 187. VLSI and ULSI Technology.
9 units (3-0-6); third term.
Prerequisites: APh/EE 9 ab, EE/APh 180 or instructor's permission.
This course is designed to cover the state-of-the-art micro/nanotechnologies for the fabrication of ULSI including BJT, CMOS, and BiCMOS. Technologies include lithography, diffusion, ion implantation, oxidation, plasma deposition and etching, etc. Topics also include the use of chemistry, thermal dynamics, mechanics, and physics. Not offered 2018-19.

BE/EE/MedE 189 ab. Design and Construction of Biodevices.
12 units (3-6-3) a = first and third terms; 9 units (0-9-0) b = third term.
Prerequisites: ACM 95/100 ab (for BE/EE/MedE 189 a); BE/EE/MedE 189 a (for BE/EE/MedE 189 b).
Part a, students will design and implement biosensing systems, including a pulse monitor, a pulse oximeter, and a real-time polymerase-chain-reaction incubator. Students will learn to program in LABVIEW. Part b is a student-initiated design project requiring instructor's permission for enrollment. Enrollment is limited to 24 students. BE/EE/MedE 189 a is an option requirement; BE/EE/MedE 189 b is not.
Instructors: Bois, Yang.

ACM/EE/IDS 217. Advanced Topics in Stochastic Analysis.
9 units (3-0-6); third term.
Prerequisites: ACM/CMS/EE/IDS 117.
The topic of this course changes from year to year and is expected to cover areas such as stochastic differential equations, stochastic control, statistical estimation and adaptive filtering, empirical processes and large deviation techniques, concentration inequalities and their applications. Examples of selected topics for stochastic differential equations include continuous time Brownian motion, Ito's calculus, Girsanov theorem, stopping times, and applications of these ideas to mathematical finance and stochastic control.
Instructor: Stuart.

EE 291. Advanced Work in Electrical Engineering.
Units to be arranged.
Special problems relating to electrical engineering. Primarily for graduate students; students should consult with their advisers.